How do you secure AI agents?

Treat every tool call as untrusted. Wrap tools behind a policy gate that validates arguments, blocks out-of-policy actions, escalates ambiguous calls to a human, and writes a tamper-evident log entry for every decision. Validation runs inline before the tool executes — not after.

How do you secure the agent lifecycle?

Cover four phases: build (policies as code, tested in CI), deploy (versioned policies pinned per agent), runtime (inline validation, approval queues, loop breakers, circuit breakers), and review (replay debugger and audit log for every incident). SafeRun is the runtime and review layer.

How do you secure agentic AI and LLM apps?

Don't try to secure the prompt — secure the actions. Constrain what tools the agent can call, what arguments are allowed, and which actions need human approval. Combine input validation with output validation on tool calls, and rate-limit by both cost and call count to contain prompt injection blast radius.

How do you host AI agents in production securely?

Run agents in an isolated workload, expose tools through a gate (not directly), keep secrets out of the agent process, log every tool call with full context, and require human approval for irreversible or high-cost actions. SafeRun ships as a drop-in SDK that does the gate, approval, and logging for you.

Start in test mode

Pillar guide

How to secure AI agents in production.

Five controls every team running agents needs — and where the gate sits in your stack. Practical, opinionated, code-first.

The problem

Agents take real actions. Logs aren't enough.

An AI agent isn't a chat box. It calls tools — refund APIs, email senders, database writes, deploy pipelines. Once it acts, the action is done. Traditional observability tells you what already broke. Securing agents means putting a decision point before the action, not a dashboard after it.

The 5 controls

What an agent security stack looks like.

1. Declarative tool-call policies

Define what each tool is allowed to do — allowed arguments, forbidden values, rate limits, cost caps — as code that lives next to your agent. Versioned, testable, deployable. Bad calls fail at the gate, not at the database.

2. Human-in-the-loop approvals

Route ambiguous or high-blast-radius actions to a human via Slack, email, or webhook before they execute. Approvers see full agent context: prompt, plan, tool, arguments, policy match.

3. Loop and circuit breakers

Detect runaway agents repeating the same tool call, cap retries, and trip a circuit breaker when cost or error rate spikes. Stops $4,500 refunds and 12-emails-to-the-same-lead before they ship.

4. Replay debugger

Step through every agent run frame by frame: model output, tool selected, arguments, policy decision, tool result, latency, cost. Turn 'the agent did something weird' into a reproducible incident.

5. Tamper-evident action log

Every decision — allowed, blocked, approved, rejected — is written to an append-only log with full context. The audit trail your security and compliance teams will ask for on day one.

Architecture

Where the gate sits.

Inline, between the agent runtime and your tools. Same process, no proxy.

Agent

LLM runtime

SafeRun

Validate · Approve · Log

Tools

APIs · DBs · Email

Three lines

Wrap the tool. That's it.

tools.tstypescript

import { guard } from "@saferun/sdk";

const safeTool = guard(tool, { policy: "production", approval: "slack" });
await safeTool.execute(args);

FAQ

Common questions about agent security.

Keep reading

Runtime action-control for AI agents

Give production agents a checkpoint before they act.

Wrap risky tool calls, pause or block what shouldn't run, and replay the decision so teams can turn each near-miss into a rule.

Start in test mode Read the guide